61 lines
1.7 KiB
Python
61 lines
1.7 KiB
Python
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import json
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from joblib import dump, load
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from matplotlib import pyplot as plt
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from sklearn import tree
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from sklearn.feature_extraction import DictVectorizer
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class DecisionTree:
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def __init__(self):
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self.clf = None
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self.vec = None
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def build(self, depth: int):
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path = "tree_data.json"
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samples = []
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results = []
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with open(path, "r") as training_file:
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for sample in training_file:
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sample, result = self.process_input(sample)
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samples.append(sample)
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results.append(result)
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self.vec = DictVectorizer()
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self.clf = tree.DecisionTreeClassifier(max_depth=depth)
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self.clf = self.clf.fit(self.vec.fit_transform(samples).toarray(), results)
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def save_model(self, clf_file, vec_file):
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dump(self.clf, clf_file)
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dump(self.vec, vec_file)
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def load_model(self, clf_file, vec_file):
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self.clf = load(clf_file)
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self.vec = load(vec_file)
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def predict_answer(self, params):
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return self.clf.predict(self.vec.transform(params).toarray())
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def plot_tree(self):
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print('Plotting tree...')
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fig = plt.figure(figsize=(36, 27))
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_ = tree.plot_tree(self.clf,
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feature_names=self.vec.get_feature_names(),
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filled=True)
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fig.savefig("decistion_tree.png")
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print('Success!')
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@staticmethod
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def process_input(line):
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data = json.loads(line.strip())
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result = data['result']
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del data['result']
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del data['food_result']
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del data['water_result']
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del data['wood_result']
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sample = data
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return sample, result
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